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import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
from .utils import (
MetricLogger,
SmoothedValue,
)
def train_one_epoch(
model: torch.nn.Module,
model_dtype: str,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
optimizer_disc: torch.optim.Optimizer,
device: torch.device,
epoch: int,
loss_scaler,
loss_scaler_disc,
clip_grad: float = 0,
log_writer=None,
lr_scheduler=None,
start_steps=None,
lr_schedule_values=None,
lr_schedule_values_disc=None,
args=None,
print_freq=20,
iters_per_epoch=2000,
):
# The trainer for causal video vae
model.train()
metric_logger = MetricLogger(delimiter=" ")
if optimizer is not None:
metric_logger.add_meter('lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('min_lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
if optimizer_disc is not None:
metric_logger.add_meter('disc_lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('disc_min_lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
if model_dtype == 'bf16':
_dtype = torch.bfloat16
else:
_dtype = torch.float16
print("Start training epoch {}, {} iters per inner epoch.".format(epoch, iters_per_epoch))
for step in metric_logger.log_every(range(iters_per_epoch), print_freq, header):
if step >= iters_per_epoch:
break
it = start_steps + step # global training iteration
if lr_schedule_values is not None:
for i, param_group in enumerate(optimizer.param_groups):
if lr_schedule_values is not None:
param_group["lr"] = lr_schedule_values[it] * param_group.get("lr_scale", 1.0)
if optimizer_disc is not None:
for i, param_group in enumerate(optimizer_disc.param_groups):
if lr_schedule_values_disc is not None:
param_group["lr"] = lr_schedule_values_disc[it] * param_group.get("lr_scale", 1.0)
samples = next(data_loader)
samples['video'] = samples['video'].to(device, non_blocking=True)
with torch.cuda.amp.autocast(enabled=True, dtype=_dtype):
rec_loss, gan_loss, log_loss = model(samples['video'], args.global_step, identifier=samples['identifier'])
###################################################################################################
# The update of rec_loss
if rec_loss is not None:
loss_value = rec_loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value), force=True)
sys.exit(1)
optimizer.zero_grad()
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
grad_norm = loss_scaler(rec_loss, optimizer, clip_grad=clip_grad,
parameters=model.module.vae.parameters(), create_graph=is_second_order)
if "scale" in loss_scaler.state_dict():
loss_scale_value = loss_scaler.state_dict()["scale"]
else:
loss_scale_value = 1
metric_logger.update(vae_loss=loss_value)
metric_logger.update(loss_scale=loss_scale_value)
###################################################################################################
# The updaet of gan_loss
if gan_loss is not None:
gan_loss_value = gan_loss.item()
if not math.isfinite(gan_loss_value):
print("The gan discriminator Loss is {}, stopping training".format(gan_loss_value), force=True)
sys.exit(1)
optimizer_disc.zero_grad()
is_second_order = hasattr(optimizer_disc, 'is_second_order') and optimizer_disc.is_second_order
disc_grad_norm = loss_scaler_disc(gan_loss, optimizer_disc, clip_grad=clip_grad,
parameters=model.module.loss.discriminator.parameters(), create_graph=is_second_order)
if "scale" in loss_scaler_disc.state_dict():
disc_loss_scale_value = loss_scaler_disc.state_dict()["scale"]
else:
disc_loss_scale_value = 1
metric_logger.update(disc_loss=gan_loss_value)
metric_logger.update(disc_loss_scale=disc_loss_scale_value)
metric_logger.update(disc_grad_norm=disc_grad_norm)
min_lr = 10.
max_lr = 0.
for group in optimizer_disc.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(disc_lr=max_lr)
metric_logger.update(disc_min_lr=min_lr)
torch.cuda.synchronize()
new_log_loss = {k.split('/')[-1]:v for k, v in log_loss.items() if k not in ['total_loss']}
metric_logger.update(**new_log_loss)
if rec_loss is not None:
min_lr = 10.
max_lr = 0.
for group in optimizer.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(lr=max_lr)
metric_logger.update(min_lr=min_lr)
weight_decay_value = None
for group in optimizer.param_groups:
if group["weight_decay"] > 0:
weight_decay_value = group["weight_decay"]
metric_logger.update(weight_decay=weight_decay_value)
metric_logger.update(grad_norm=grad_norm)
if log_writer is not None:
log_writer.update(**new_log_loss, head="train/loss")
log_writer.update(lr=max_lr, head="opt")
log_writer.update(min_lr=min_lr, head="opt")
log_writer.update(weight_decay=weight_decay_value, head="opt")
log_writer.update(grad_norm=grad_norm, head="opt")
log_writer.set_step()
if lr_scheduler is not None:
lr_scheduler.step_update(start_steps + step)
args.global_step = args.global_step + 1
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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